An entropy inspired measure for evaluating ontology modularization

Ontology modularization has received growing interest from the research community lately, since it has been shown to support a variety of tasks for ontology engineers and users alike, such as ontology design, maintenance, reuse; knowledge selection and integration. Most of the research efforts have concentrated on approaches to extract modules, or generate partitions from an input ontology. However these approaches are influenced by different definitions of ontology modularization and thus tend to vary with respect to the concepts and properties in the ontology that should define the module, and on the characteristics that modules should exhibit, which often depend on the task for which the modularization process is performed. This diversity of approaches makes the comparative evaluation of the output of different modularization processes hard to perform. In this paper, we propose an entropy inspired measure for modularization, Integrated Ontology Entropy, that approximates the information content of modules, and hence provides a profile for the module generated. This measure is independent of the modularization technique used, and is calculated as a function of the number of edges connecting the named concepts in the ontology, when a graph representation of the ontology is utilized. In the paper we apply this measure to the modules generated by different modularization techniques and we empirically explore how the measure behaves given the different characteristics of the generated modules, such as the degree of redundancy and the level of connectedness